Modern consumer electronics, such as cellular phones, digital cameras, and music players, are packing more integrated circuits into an ever-shrinking physical space with the expectations of decreasing cost. Numerous technologies have been developed to meet these requirements. One cornerstone for consumer electronics to continue proliferation into everyday life is the non-volatile storage of information such as cellular phone numbers, and digital pictures, music files.
Moreover, in the coming years, portable systems will demand even more nonvolatile memories, either with high density and very high writing throughput for data storage application or with fast random access for code execution in place. The flexibility and cost make the Flash memory a largely utilized and mature technology for most of the nonvolatile memory applications. Today, Flash sales represent a considerable amount of the overall semiconductor market.
Integrated circuits including non-volatile memories are made in and on wafers by extremely complex systems that require the coordination of hundreds or even thousands of precisely controlled processes to produce a finished semiconductor wafer. Each finished semiconductor wafer has hundreds to tens of thousands of integrated circuits, each worth hundreds or thousands of dollars.
The ideal would be to have every one of the integrated circuits on a wafer functional and within specifications, but because of the sheer numbers of processes and minute variations in the processes, this rarely occurs. “Yield” is the measure of how many “good” integrated circuits there are on a wafer divided by the total number of integrated circuits formed on the wafer divided by the maximum number of possible good integrated circuits on the wafer. A 100% yield is extremely difficult to obtain because minor variations, due to such factors as timing, temperature, and materials, substantially affect a process. Further, one process often affects a number of other processes, often in unpredictable ways.
In a manufacturing environment, the primary purpose of experimentation is to increase the yield. Experiments are performed in-line and at the end of the production line with both production wafers and experimental wafers. However, yield enhancement methodologies in the manufacturing environment produce an abundance of very detailed data for a large number of wafers on processes subject only to minor variations. Major variations in the processes are not possible because of the time and cost of using production equipment and production wafers. Setup times for equipment and processing time can range from weeks to months, and processed wafers can each contain hundreds of thousands of dollars worth of integrated circuits.
The learning cycle for the improvement of systems and processes requires coming up with an idea, formulating a test(s) of the idea, testing the idea to obtain data, studying the data to determine the correctness of the idea, and developing new ideas based on the correctness of the first idea. The faster the correctness of ideas can be determined, the faster new ideas can be developed. Unfortunately, the manufacturing environment provides a slow learning cycle because of manufacturing time and cost.
Recently, the great increase in the complexity of integrated circuit manufacturing processes and the decrease in time between new product conception and market introduction have both created the need for speeding up the learning cycle.
This has been accomplished in part by the unique development of the integrated circuit research and development environment. In this environment, the learning cycle has been greatly speeded up and innovative techniques have been developed that have been extrapolated to high volume manufacturing facilities.
To speed up the learning cycle, processes are speeded up and major variations are made to many processes, but only a few wafers are processed to reduce cost. The research and development environment has resulted in the generation of tremendous amounts of data and analysis for all the different processes and variations. This, in turn, has required a large number of engineers to do the analysis. With more data, the answer always has been to hire more engineers.
However, this is not an acceptable solution for major problems.
The problems include, but are not limited to, memory manufacturing, test, sort, verification failure analysis, and process improvements in both embedded and stand-alone applications. As the demand for memory increases, especially for non-volatile memory, improvements are required to reliably and efficiently provide the memory solutions.
Solutions to these problems have been long sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art.